# Eigen Values Features for the Classification of Brain Signals corresponding to 2D and 3D Educational Contents

## TL;DR

This study introduces a novel brain signal classification method using eigenvalues of covariance matrices to distinguish correct from incorrect answers during 2D and 3D educational content engagement, assessing their impact on learning and memory.

## Contribution

It proposes a new eigenvalue-based feature extraction technique for classifying brain signals related to educational content comprehension, comparing 2D and 3D effects.

## Key findings

- High classification accuracy with KNN and SVM classifiers
- No significant difference between 2D and 3D content on learning outcomes
- Eigenvalues effectively distinguish correct and incorrect answer states

## Abstract

In this paper, we have proposed a brain signal classification method, which uses eigenvalues of the covariance matrix as features to classify images (topomaps) created from the brain signals. The signals are recorded during the answering of 2D and 3D questions. The system is used to classify the correct and incorrect answers for both 2D and 3D questions. Using the classification technique, the impacts of 2D and 3D multimedia educational contents on learning, memory retention and recall will be compared. The subjects learn similar 2D and 3D educational contents. Afterwards, subjects are asked 20 multiple-choice questions (MCQs) associated with the contents after thirty minutes (Short-Term Memory) and two months (Long-Term Memory). Eigenvalues features extracted from topomaps images are given to K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) classifiers, in order to identify the states of the brain related to incorrect and correct answers. Excellent accuracies obtained by both classifiers and by applying statistical analysis on the results, no significant difference is indicated between 2D and 3D multimedia educational contents on learning, memory retention and recall in both STM and LTM.

---
Source: https://tomesphere.com/paper/1904.13221